We present an innovative approach for solving Four Dimensional Variational Data Assimilation (4D-VAR DA) problems. The approach we consider starts from a decomposition of the physical domain; it uses a partitioning of the solution and a modified regularization functional describing the 4D-VAR DA problem on the decomposition. We provide a mathematical formulation of the model and we perform a feasibility analysis in terms of computational cost and of algorithmic scalability. We use the scale-up factor which measure the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Shallow Water Equations).
On the problem-decomposition of scalable 4D-Var Data Assimilation models
Carracciuolo;
2015
Abstract
We present an innovative approach for solving Four Dimensional Variational Data Assimilation (4D-VAR DA) problems. The approach we consider starts from a decomposition of the physical domain; it uses a partitioning of the solution and a modified regularization functional describing the 4D-VAR DA problem on the decomposition. We provide a mathematical formulation of the model and we perform a feasibility analysis in terms of computational cost and of algorithmic scalability. We use the scale-up factor which measure the performance gain in terms of time complexity reduction. We verify the reliability of the approach on a consistent test case (the Shallow Water Equations).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
